DataRobot-Research/drdocs huggingface dataset:
---
title: Intermittent target leakage
description: DataRobot's target leakage detection explained.
---

# Intermittent target leakage {: #intermittent-target-leakage }


<span style="color:red;font-size: 1rem"> `Robot 1`</span>

**Why might target leakage show intermittently?**

Hi team! A student in the DataRobot for Data Scientists class created a ModelID Categorical Int feature from the standard class “fastiron 100k data.csv.zip ” file and it flagged as [Target Leakage](data-quality#target-leakage) on his first run under manual.

When he tried to do it again, the platform did not give the yellow triangle for target leakage but the Data Quality Assessment box did flag a target leakage feature.

His questions are:

1. Why is DataRobot showing the target leakage intermittently?

2. The original ModelID as a numeric int did not cause a target leakage flag and also when he included that Parent feature with the child feature (ModelID as categorical int) it did not flag as Target Leakage&mdash;why is that?


<span style="color:red;font-size: 1rem"> `Robot 2`</span>

At a quick glance, it sounds like the user created a new feature `ModelID (Categorical Int)` via Var Type transform, and then kicked off manual mode in which the created feature received a calculated ACE importance scores. The importance scores passed our target leakage threshold and therefore Data Quality Assessment tagged the feature as potential leakage. 


<span style="color:red;font-size: 1rem"> `Robot 2`</span>

After looking at the project, I see that there was not a feature list called "Informative Features - Leakage Removed" created, meaning it didn't pass the "high-risk" leakage threshold value, and therefore was tagged as "moderate-risk" leakage feature.

I found the `/eda/profile/` values from the Network Console for the project for the specific feature `ModelId (Categorical Int)`. The calculated ACE importance score (Gini Norm metric) for that created feature is about 0.8501:

`target_leakage_metadata: {importance: {impact: "moderate", reason: "importance score", value: 0.8501722785995578}}`

And... yeah, the hard-coded moderate-risk threshold value in the code is in fact 0.85.

<span style="color:red;font-size: 1rem"> `Robot 2`</span>

You can let the user know that changing a Numeric feature to Categorical var type can lead to potentially different univariate analysis results with regards to our Data page Importance score calculations. The Importance scores just narrowly passed our moderate-risk detected Target Leakage threshold value. Hope that helps.

<span style="color:red;font-size: 1rem"> `Robot 1`</span>

Thanks for such a detailed feedback!

<span style="color:red;font-size: 1rem"> `Robot 2`</span>

Any time, any further questions about Target Leakage let the TREX (trust and explainability) team know (or ping me, since I helped work on it)!
---
title: Version 7.2.x
description: DataRobot Release 7.2 release announcements index page.

---

# Version 7.2.x {: #version-72x }

Following are the AutoML, time series, and MLOps announcements for the features that make up DataRobot's 7.2.x releases, first released _September 13, 2021_.

* [v7.2.x AutoML](v7.2.0-aml)
* [v7.2.x time series](v7.2.0-ats)
* [v7.2.x MLOps](v7.2-mlops)
* [Maintenance release notes](7.2-maintenance/index) for DataRobot v7.2.x.
# DRUM CLI tool {: #drum-cli-tool }

DataRobot user model (DRUM) is a CLI tool that allows you to work with Python, R, and Java custom models and to quickly test [custom tasks](cml-custom-tasks), [custom models](custom-models/index), and [custom environments](custom-environments) locally before uploading into DataRobot. Because it is also used to run custom tasks and models inside of DataRobot, if they pass local tests with DRUM, they are compatible with DataRobot. You can download DRUM from <a target="_blank" href="https://pypi.org/project/datarobot-drum/">PyPi</a>. 

DRUM can also:

* Run performance and memory usage testing for models.

* Perform model validation tests (for example, checking model functionality on corner cases, like null values imputation).

* Run models in a Docker container.

You can install DRUM for [Ubuntu](#drum-on-ubuntu), [Windows](#drum-on-windows-with-wsl2), or [MacOS](#drum-on-mac).

!!! note

	DRUM is not regularly tested on Windows or Mac. These steps may differ depending on the configuration of your machine.
---
title: Self-Managed AI Platform releases
description: An archive of release announcements published for DataRobot's Self-Managed AI Platform (on-premise and VPC).

---

# Self-Managed AI Platform releases {: #self-managed-ai-platform-releases }

View details, by release version, of AutoML, time series, MLOps, and Data Prep feature announcements, enhancements, and fixed and known issues. These notes were provided for DataRobot's Self-Managed AI Platform deployment options.

Version | Release date
------- | -------------
[DataRobot 9.0](v9.0/index) | _March 29, 2023_
[DataRobot 8.0](v8.0/index) | _March 14, 2022_
[DataRobot 7.3](v7.3/index) | _December 13, 2021_
[DataRobot 7.2](v7.2/index) | _September 13, 2021_
[DataRobot 7.1](v7.1/index) | _June 14, 2021_
[DataRobot 7.0](v7.0/index) | _March 15, 2021_
---
title: DataRobot in 5
description: A short overview of the steps involved in building and deploying models in DataRobot.

---

# DataRobot in 5 {: #datarobot-in-5 }

Building and deploying models in DataRobot&mdash;regardless of the data handling, modeling options, prediction methods, and deployment actions&mdash;comes down to the same five steps:

1. Work with data.
2. Build experiments.
3. Investigate models.
4. Make predictions.
5. Deploy models.

Review the steps below, which link out to more complete descriptions, to get a quick understanding of how to be successful in DataRobot.

## 1: Work with data {: #1-work-with-data }

[Working with data](gs-wb-data) involves importing (or connecting), exploring, and preparing your data. Three  steps and your data is ready for modeling.

![](images/gs-data-3.png)

## 2: Build experiments {: #2-build-experiments }

To [build experiments](gs-wb-experiments), create a use case, add data, and start modeling.

![](images/wb-exp-1.png)

## 3: Evaluate models {: #3-evaluate-models }

DataRobot's Leaderboard shows you all the models built for your experiment. Click on any model to [access visualizations](gs-wb-experiments#evaluate-models) for evaluation and to inform further experimentation.

![](images/wb-5-step-1.png)

## 4: Make predictions {: #4-make-predictions }

Once you have selected a model, you can make predictions with it to assess model performance before deploying.

![](images/wb-5-step-2.png)

## 5: Deploy models {: #5-deploy-models }

 At this time, model deployment options are available in the DataRobot Classic interface. If you are in Workbench, transfer to DataRobot Classic and find your experiment in the [project management center](manage-projects#manage-projects-control-center){ target=_blank }. From the Leaderboard, select the model to deploy and choose [**Predict > Deploy**](deploy-model#deploy-from-the-leaderboard){ target=_blank }.

 ![](images/wb-5-step-3.png)


## Next steps {: #next-steps }

 From here, you can:

 * [Create DataRobot Notebooks](gs-code).
 * [Leverage DataRobot's AI Accelerators](gs-ai).
---
title: Version 7.3.x maintenance releases
description: Maintenance releases for the DataRobot Release 7.3 major release.
---

# Version 7.3.x maintenance releases {: #version-73x-maintenance-releases }

The following maintenance release notes include some fixed issues in the DataRobot Self-Managed AI Platform platform. See also the [features introduced in v7.3.0](v7.3/index), which was introduced _December 13, 2021_.


Version |	Release date
------- | -----------
* [v7.3.6](v7.3.6-aml) | *May 4, 2022*
* [v7.3.5)](v7.3.5-aml) | *February 18, 2022*
* [v7.3.4)](v7.3.4-aml) | *February 7, 2022*
* [v7.3.3)](v7.3.3-aml) | *January 17, 2022*
* [v7.3.2)](v7.3.2-aml) | *December 21, 2021*
* [v7.3.1)](v7.3.1-aml) | *December 16, 2021*
* [v7.3.0)](v7.3.0-aml) | *December 13, 2021*
---
title: Public preview features
description: Read preliminary documentation for data-related features currently in the DataRobot public preview pipeline.
section_name: Workbench
maturity: public-preview
---

# Data preparation public preview features {: #data-preparation-public-preview-features }

{% include 'includes/pub-preview-notice-include.md' %}

## Available public preview documentation {: #available-public-preview-documentation }

Public preview for... | Describes...
----- | ------
[Publish recipes with smart downsampling](wb-downsample)  |  Use smart downsampling to reduce the size of your output dataset when publishing a wrangling recipe.
---
title: Composable ML
description: Documentation for Composable Machine Learning (ML), including a Quickstart, overview, and instructions for editing or crating blueprints, tasks, and environments.

---

# Composable ML {: #composable-ml }

Documentation for Composable Machine Learning (ML) includes a Quickstart, overview, and instructions for editing or crating blueprints, tasks, and environments.

Topic | Describes...
----- | ------------
[Overview](cml-overview) | How Composable ML works and provides sample use cases.
[Quickstart](cml-quickstart) | A simple example to try out Composable ML.
[Blueprint modification](cml-blueprint-edit) | DataRobot blueprints and describes how to modify them.
[Custom tasks](cml-custom-tasks) | Creating custom tasks.
[Custom environments ](cml-custom-env) | Creating custom environments.
[DRUM CLI tool](cml-drum) | How to install and use the DataRobot User Models (DRUM) CLI tool.
[Composable ML reference](cml-ref/index) | Blueprints in the AI Catalog and metadata for custom models and tasks.
[Sentiment analysis example](cml-sentiment-example) | A tip for capturing Text sentiments using Composable ML.
---
title: Bias and Fairness overview
description: Provides a high-level of overview of bias in machine learning and DataRobot's detection and prevention tools.

---

# Bias and Fairness overview {: #bias-and-fairness-overview }

In DataRobot, bias represents the difference between a model's predictions for different populations (or groups) while fairness is the measure of the model's bias. More specifically, it provides methods to calculate fairness for a binary classification model and to identify any biases in the model's predictive behavior. 

Fairness metrics in modeling describe the ways in which a model can perform differently for distinct groups within data. Those groups, when they designate groups of people, might be identified by protected or sensitive characteristics, such as race, gender, age, and veteran status.

The largest source of bias in an AI system is the data it was trained on. That data might have historical patterns of bias encoded in its outcomes. Bias might also be a product not of the historical process itself but of data collection or sampling methods misrepresenting the ground truth.


See the [index](b-and-f/index) for links to the settings and tools available on DataRobot to enable bias mitigation.

## Additional resources {: #additional-resources }

* Blog post: [_Bias and Fairness as Dimensions of Trusted AI_](https://www.datarobot.com/trusted-ai-101/ethics/bias-and-fairness/){ target=_blank } resource page for more detail.

* Part one of a three-part series in partnership with Amazon Web Services: [How to Build & Govern Trusted AI Systems](https://www.datarobot.com/blog/how-to-build-govern-trusted-ai-systems-people/){ target=_blank }

* Podcast: [Moving Past Artificial Intelligence To Augmented Intelligence](https://www.moreintelligent.ai/podcasts/moving-past-artificial-intelligence-to-augmented-intelligence/){ target=_blank }
---
title: Multiple DR Reduced Features lists
description: How to have multiple Reduced Features lists in one project.
---

# Multiple DR Reduced Features lists {: #multiple-reduced-feature-lists }

<span style="color:red;font-size: 1rem"> `Robot 1`</span>

**Can I have multiple DR Reduced Features lists for one project?**

Hi team, can I have multiple [DR Reduced Features](feature-lists#automatically-created-feature-lists) lists for one project? I would like to create the DR Reduced Features based on a feature list after Autopilot completes. But I don’t see DR Reduced Feature created when I retrain the model on a new feature list through “Configure Modeling Settings”.

<span style="color:red;font-size: 1rem"> `Robot 2`</span>

You can have a new reduced feature list but only if there is a new most accurate model. We don't base the recommendation stages on the specific Autopilot run. You can take the new best model and, under the **Deploy** tab, [prepare it for deployment](model-rec-process#prepare-a-model-for-deployment). This will run through all of the stages, including a reduced feature list stage. Note that not all feature lists can be reduced, so this stage might be skipped.

<span style="color:red;font-size: 1rem"> `Robot 3`</span>

Robot 1, you can manually make a reduced featurevlist from [Feature Impact](feature-impact#create-a-new-feature-list) for any model:

1. Run Feature Impact.
2. Hit "Create feature list".
3. Choose the number of features you want.
3. Check “exclude redundant features” option (it only shows up if there _are_ redundant features.
4. Name the feature list.
5. Click to create!
---
title: Predict lumber prices with Ready Signal and time series forecasts
description: Use Ready Signal to add external control data, such as census and weather data, to improve time series predictions.

---

# Predict lumber prices with Ready Signal and time series forecasts {: #predict-lumber-prices-with-ready-signal-and-time-series-forecasts }

[Access this AI accelerator on GitHub <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced-experimentation/Ready_Signal_TS/DataRobot_RXA.ipynb){ .md-button }

In this accelerator, you will explore how to bring external data from Ready Signal to help augment your time series forecasting accuracy.

Ready Signal is an AI-powered data platform that provides access to over 500 normalized, aggregated, and automatically updated data sources for predictive modeling, experimentation, business intelligence, and other data enrichment needs. The data catalog includes micro/macro-economic indicators, labor statistics, demographics, weather, and more. Its AI recommendation engine and auto feature engineering capabilities make it easy to integrate with existing data pipelines and analytics tooling, accelerating and enhancing how relevant third-party data is leveraged.

Here, DataRobot provides an example of predicting lumber price combined with the most relevant external data automatically identified by ReadySignal based on correlation with the target variable. The workflow can be applied to any time series forecasting project.

If you are interested in learning more about how Ready Signal and DataRobot together can help your time series project, please reach out to Matt Schaefer (matt.schaefer@readysignal.com) or anyone else in the author list.
---
title: Download Scoring Code from the Leaderboard (Legacy)
description: Download a Scoring Code JAR file directly from the Leaderboard as a legacy user.

---

# Download Scoring Code for legacy users {: #download-scoring-code-for-legacy-users }

Models displaying the SCORING CODE [indicator](leaderboard-ref#tags-and-indicators) on the Leaderboard are available for Scoring Code download.

!!! info "Availability information"
     The ability to download Scoring Code for a model from the Leaderboard depends on the [MLOps configuration](pricing) for your organization. Legacy users will see the option described below. MLOps users can download Scoring Code [from the Leaderboard](sc-download-leaderboard) and directly [from a deployment](sc-download-deployment).

Navigate to the **Predict > Downloads** tab, where you can select a download option and access a link to the up-to-date Java API documentation.

![](images/scorecode-source.png)

There are two download options for Scoring Code:

|  Selection  | Description |
|-------------|-------------|
| Binary  | These are compiled Java executables, which include all dependencies and can be used to make predictions.|
| Source (Java source code files) | These are a non-obfuscated version of the model; they cannot be used to score the model since they are not compiled and dependency packages are not included. Use the source files to explore the model’s decision-making process. |

Additional information about the Java API can be found in the [DataRobot javadocs](https://javadoc.io/doc/com.datarobot/datarobot-prediction/2.0.11"){ target=_blank }.
---
title: Public network access for custom models
description: Access any fully qualified domain name (FQDN) in a public network so that the model can leverage third-party services, or disable public network access to isolate a model from the network and block outgoing traffic.
section_name: MLOps
maturity: public-preview

---

# Public network access for custom models {: #full-network-access-for-custom-models }

!!! info "Availability information"
    Public network access for custom models is off by default. Contact your DataRobot representative or administrator for information on enabling this feature.

    <b>Feature flag:</b> Enable Public Network Access for all Custom Models

Now available as a Public Preview feature, you can enable full network access for any custom model. When you create a custom model, you can access any fully qualified domain name (FQDN) in a public network so that the model can leverage third-party services. Alternatively, you can disable public network access if you want to isolate a model from the network and block outgoing traffic to enhance the security of the model.

To manage a custom model's network access:

1. Navigate to **Model Registry > Custom Model Workshop**.

2. On the **Models** tab, click the model you want to manage and then click the **Assemble** tab.

3. On the custom model's **Assemble Model** page, under **Resource Settings**, review the **Network access** setting:

    ![](images/network-access-setting.png)

    Setting | Description
    --------|------------
    Public  | (Default) The custom model can access any fully qualified domain name (FQDN) in a public network to leverage third-party services.
    None    | The custom model is isolated from the public network and cannot access third party services.

    !!! tip
        You can also see the **Network access** setting in the custom model's model package on the **Model Registry > Model Packages** page. Click the custom model package, and then, on the **Package Info** tab, scroll down to the **Resource Allocation** section.

4. If you want to change the Network access setting, click the edit icon (![](images/icon-pencil.png)); then, in the **Update resource settings** dialog box, change the **Network access** option and click **Save**.

    ![](images/update-network-access-setting.png)

    !!! important
        You can only update resource settings in the most recent model version. Additionally, if the most recent model version was registered or deployed, you cannot apply the updated resource settings, and you'll encounter a "frozen version" warning if you try to save the changes: 

        ![](images/frozen-version-warning.png)

        If you encounter this warning, create a new custom model version, update the resource settings, and then register and deploy the model with the correct settings.
---
title: Predictions
description: How to make predictions for a Workbench model.

---

#  Predictions {: #predictions }

The following sections help to understand Workbench predictions:

Topic   |   Describes...
---------|-----------------
[Make predictions](wb-predict)  | After you create an experiment and train models, you can upload scoring data, make predictions, and download the results.
[Reference and FAQ](wb-predict-ref)  | Questions and tips for working with predictions in Workbench.
# Version 8.0.12 {: #version-8012 }

_February 27, 2023_

The DataRobot v8.0.12 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v8.0.0 release notes for:

* [Features introduced in v8.0.0](v8.0.0-aml)

In addition to the issues listed below, DataRobot addressed several outstanding security issues. This release/update is part of DataRobot's continual effort to secure the product and platform.

## Issues fixed in v8.0.12 {: #issues-fixed-in-v8012 }

The following issues have been fixed since release v8.0.11:

### Core modeling {: #core-modeling }

- MODEL-9497: Fixes the Composable ML task parameter validation logic. The submitted task parameter is now validated against both project settings and corresponding allowed values.

### Data management {: #data-management }

- DM-6623: Fixes an issue causing the **Grant access** button to not appear during ADLS authentication.

### Model management {: #model-management }

- MMM-11977: Adds the parameter `MMM_MODMON_PREDICTION_RESULTS_DELETE_QUERY_BATCH_SIZE` to make batch size configurable for `PredictionResultCleanup`.

### Predictions {: #predictions }

- PRED-7826: Fixes an issue where a schema was required for JDBC connections that do not require one.


_All product and company names are trademarks&trade; or registered&reg; trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them_.
---
title: DRUM CLI tool
description: DataRobot Model Runner (DRUM) is a tool that allows you to work with Python, R, and Java custom models and to quickly test custom tasks.

---

{% include 'includes/drum-tool.md' %}

{% include 'includes/drum-for-ubuntu.md' %}

{% include 'includes/drum-for-mac.md' %}

### Use DRUM on Mac {: #use-drum-on-mac }

To test a task locally, run the `drum fit` command. For example, in a binary classification project:

1. Ensure that the `conda` environment **DR-custom-tasks** is activated.

2. Run the `drum fit` command (replacing placeholder folder names in `< >` brackets with actual folder names):

	```
	drum fit --code-dir <folder_with_task_content> --input <test_data.csv>  --target-type binary --target <target_column_name> --docker <folder_with_dockerfile> --verbose
	```
	
	For example:

	```
	drum fit --code-dir datarobot-user-models/custom_tasks/examples/python3_sklearn_binary --input datarobot-user-models/tests/testdata/iris_binary_training.csv --target-type binary --target Species --docker datarobot-user-models/public_dropin_environments/python3_sklearn/ --verbose
	```

!!! tip
	To learn more, you can view available parameters by typing `drum fit --help` on the command line.

{% include 'includes/drum-for-windows.md' %}

### Use DRUM on Windows {: #use-drum-on-windows }

1. From the command line, open an Ubuntu terminal.

2. Use the following commands to activate the environment:

	```
	cd $HOME
	source DR-custom-tasks-pyenv/bin/activate
	```

3. Run the `drum fit` command in an Ubuntu terminal window (replacing placeholder folder names in `< >` brackets with actual folder names):

	```
	drum fit --code-dir <folder_with_task_content> --input <test_data.csv>  --target-type binary --target <target_column_name> --docker <folder_with_dockerfile> --verbose
	```

	For example:

	```
	drum fit --code-dir datarobot-user-models/custom_tasks/examples/python3_sklearn_binary --input datarobot-user-models/tests/testdata/iris_binary_training.csv --target-type binary --target Species --docker datarobot-user-models/public_dropin_environments/python3_sklearn/ --verbose
	```